儿童OSAHS疾病严重程度影响因素分析及临床预测价值

Influencing factors and clinical predictive value for disease severity in pediatric obstructive sleep apnea-hypopnea syndrome

摘要

目的 分析儿童阻塞性睡眠呼吸暂停低通气综合征(obstructive sleep apnea-hypopnea syndrome, OSAHS)疾病严重程度的影响因素，并评估其临床预测价值。方法 选取2023年1月至2024年12月在石家庄人民医院就诊的OSAHS患儿182例为研究对象，采集人口学信息、多导睡眠监测及血常规检测结果，计算系统性炎症指标。采用差异性比较、Spearman等级相关、随机森林模型和logistic回归模型分析OSAHS患儿疾病严重程度的影响因素，并基于影响因素构建预测模型，通过受试者工作特征(receiver operating characteristic, ROC)曲线评价预测效能。结果 轻度和中、重度OSAHS患儿在体质量指数(body mass index, BMI)、早产史及系统性炎症指标上差异有统计学意义(P<0.05)。Spearman等级相关及随机森林模型分析结果显示，中性粒细胞-淋巴细胞比值(neutrophil-lymphocyte ratio, NLR)是6种系统性炎症指标中对疾病严重程度影响最大的变量。logistic回归分析结果显示，BMI(OR=1.383, 95%CI: 1.201~1.592)和NLR(OR=1.575, 95%CI: 1.262~1.965)越大，OSAHS患儿疾病为中、重度的风险越高(P<0.05)。ROC曲线分析结果显示，构建的预测模型预测OSAHS患儿疾病严重程度的曲线下面积为0.800(95%CI: 0.734~0.856)。结论 BMI和NLR是OSAHS患儿疾病严重程度的影响因素，基于二者构建的预测模型预测效能较好，有助于早期评估儿童OSAHS疾病严重程度，指导临床诊疗工作的开展。

Abstract

Objective To analyze the influencing factors for disease severity in pediatric obstructive sleep apnea-hypopnea syndrome (OSAHS) and assess their clinical predictive value. Methods A total of 182 children with OSAHS treated at Shijiazhuang People's Hospital from January 2023 to December 2024 were enrolled. Demographic information, polysomnography data, and complete blood count results were collected, and systemic inflammatory indicators were calculated. Differences between groups, Spearman rank correlation, random forest model, and logistic regression analysis were used to identify factors influencing disease severity in children with OSAHS, and a prediction model was constructed based on these factors. The predictive performance was evaluated using the receiver operating characteristic (ROC) curve. Results Statistically significant differences were observed in body mass index (BMI), history of premature birth, and systemic inflammatory indicators between children with mild OSAHS and those with moderate to severe OSAHS (P<0.05). Spearman rank correlation and random forest model analysis revealed that the neutrophil-to-lymphocyte ratio (NLR) was the most influential variable among the six systemic inflammatory indicators affecting disease severity. Logistic regression analysis showed that higher BMI (OR=1.383, 95%CI: 1.201-1.592) and higher NLR (OR=1.575, 95%CI: 1.262-1.965) were associated with an increased risk of moderate to severe OSAHS (P<0.05). ROC curve analysis demonstrated that the constructed prediction model achieved an area under the curve of 0.800 (95%CI: 0.734-0.856) for predicting disease severity. Conclusion BMI and NLR are influencing factors for disease severity in children with OSAHS. The prediction model based on these two factors demonstrates good predictive performance, which may facilitate early assessment of disease severity and guide clinical management.

关键词

阻塞性睡眠呼吸暂停通气综合征 / 疾病严重程度 / 系统性炎症指标 / 体质量指数 / 预测模型

Key words

Obstructive sleep apnea hypopnea syndrome / Disease severity / Systemic inflammation indicator / Body mass index / Prediction model

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基金资助

河北省卫生健康委科研基金项目资助(20241587)